Publication | Closed Access
Efficient Cache-Supported Path Planning on Roads
24
Citations
22
References
2015
Year
EngineeringGlobal PlanningPathfindingSocial SciencesNavigation (Computer Networking)Navigation (Cognitive Neuroscience)Trajectory PlanningGlobal Positioning SystemSystems EngineeringCombinatorial OptimizationComputational GeometryNavigation (Marine Navigation)Path PlanningComputer ScienceMobile ComputingRoute PlanningPath Planning ServicePlanningTransportation Systems
Owing to the wide availability of the global positioning system (GPS) and digital mapping of roads, road network navigation services have become a basic application on many mobile devices. Path planning, a fundamental function of road network navigation services, finds a route between the specified start location and destination. The efficiency of this path planning function is critical for mobile users on roads due to various dynamic scenarios, such as a sudden change in driving direction, unexpected traffic conditions, lost or unstable GPS signals, and so on. In these scenarios, the path planning service needs to be delivered in a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">timely</i> fashion. In this paper, we propose a system, namely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Path Planning by Caching (PPC)</i> , to answer a new path planning query in real time by efficiently caching and reusing historical queried-paths. Unlike the conventional cache-based path planning systems, where a queried-path in cache is used only when it matches perfectly with the new query, PPC leverages the partially matched queries to answer part(s) of the new query. As a result, the server only needs to compute the unmatched path segments, thus significantly reducing the overall system workload. Comprehensive experimentation on a real road network database shows that our system outperforms the state-of-the-art path planning techniques by reducing 32 percent of the computation latency on average.
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